Among various kinds of sound localization methods, generalized cross correlation (GCC) [1 3] was used for robotic applications [4] but it is not robust under multiple sources environment.
The first phase estimates one DOA, even in a multiple-source environment, by 1) applying a coherence threshold between the DOAs estimated by each microphone pair and by 2) taking advantage of the incomplete overlap of simultaneous speech.
Further, reported annoyance to multiple noise source environments can be higher than predicted by conventional Leq.
This configuration has been widely adopted due to its potential performance in a general multiple noise source environment.
For multiple sound sources environment, the GCC function should have multiple peaks [28].
Improvements on the performance in the multiple sources and reverberant environment have also been discussed [5, 6].
In our future work, we will develop range estimation algorithm without 1D search and incorporate a reverberated signal model to localize multiple sources in reverberant environment.
And on Friday, I featured Lytics, a service that tackles the problem that comes with getting value out of data from multiple sources such as mobile environments, the web, email, social, third-party APIs and commerce environments.
Lytics, which is in the process of finalizing a seed round of funding, tackles the problem that comes with getting value out of data from multiple sources such as mobile environments, the web, email, social, third-party APIs and commerce environments.
The relationship between traits and source environments was evaluated by considering correlations between traits and the climates of seed sources and by multiple regressions of trait values on climatic variables.
